{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 加载测试结果数据，并分类为Codec与Json数据集\n",
    "```text\n",
    "Codec,C03-K02,725\n",
    "Json,C03-K02,2515\n",
    "Codec,C16-K08,1390\n",
    "Json,C16-K08,13196\n",
    "Codec,C32-K16,2470\n",
    "Json,C32-K16,26248\n",
    "Codec,C63-K32,3063\n",
    "Json,C63-K32,49477\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "csvFile = open(\"./report.out\", \"r\")\n",
    "reader = csv.reader(csvFile)\n",
    "\n",
    "codec_list = []\n",
    "json_list = []\n",
    "title_list = []\n",
    "for item in reader:\n",
    "    if item[0] == 'Codec':\n",
    "        codec_list.append(int(item[2]))\n",
    "    if item[0] == 'Json':\n",
    "        json_list.append(int(item[2]))\n",
    "    it = item[1]\n",
    "    if it not in title_list:\n",
    "        title_list.append(it)\n",
    "csvFile.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 定义参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "XLABEL = \"Count-Key\"\n",
    "YLABEL = \"耗时(ns/op)\"\n",
    "TITLE = \"Y3 vs. JSON\"\n",
    "\n",
    "# bar_ylim = 70000\n",
    "# barh_xlim = 25\n",
    "\n",
    "bar_ylim = 18000\n",
    "barh_xlim = 25\n",
    "\n",
    "from os import environ\n",
    "\n",
    "if 'bar_ylim' in environ:\n",
    "    bar_ylim = int(environ['bar_ylim'])\n",
    "    \n",
    "if 'barh_xlim' in environ:\n",
    "    barh_xlim = int(environ['barh_xlim'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 绘制Y3与Json在不同数key数量集合中的性能差异"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3150x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['Arial Unicode MS'] \n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "from IPython.core.pylabtools import figsize\n",
    "figsize(10.5, 4)\n",
    "\n",
    "plt.rcParams['savefig.dpi'] = 300 \n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "\n",
    "label_list = title_list\n",
    "num_list1 = codec_list \n",
    "num_list2 = json_list\n",
    "x = range(len(num_list1))\n",
    "\n",
    "rects1 = plt.bar(x, height=num_list1, width=0.4, alpha=0.8, color='red', label=\"Y3\")\n",
    "rects2 = plt.bar([i + 0.4 for i in x], height=num_list2, width=0.4, color='green', label=\"JSON\")\n",
    "# plt.ylim(0, 70000)\n",
    "plt.ylim(0, bar_ylim)\n",
    "plt.ylabel(YLABEL)\n",
    "\n",
    "plt.xticks([index + 0.2 for index in x], label_list)\n",
    "plt.xlabel(XLABEL)\n",
    "plt.title(TITLE)\n",
    "plt.legend()\n",
    "\n",
    "for rect in rects1:\n",
    "    height = rect.get_height()\n",
    "    plt.text(rect.get_x() + rect.get_width() / 2, height+1, str(height), ha=\"center\", va=\"bottom\")\n",
    "for rect in rects2:\n",
    "    height = rect.get_height()\n",
    "    plt.text(rect.get_x() + rect.get_width() / 2, height+1, str(height), ha=\"center\", va=\"bottom\")\n",
    "plt.savefig('./report1.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 绘制Y3与Json比较结果的拆线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3150x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['Arial Unicode MS'] \n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "from IPython.core.pylabtools import figsize\n",
    "figsize(10.5, 4)\n",
    "\n",
    "multiple = np.array(json_list) / np.array(codec_list)\n",
    "multiple = np.around(multiple, decimals=1)\n",
    "\n",
    "plt.barh(range(multiple.size), multiple, height=0.7, color='steelblue', alpha=0.8)\n",
    "plt.yticks(range(multiple.size), title_list)\n",
    "plt.xlim(0,barh_xlim)\n",
    "plt.xlabel(XLABEL)\n",
    "plt.title(TITLE)\n",
    "for x, y in enumerate(multiple):\n",
    "    plt.text(y + 0.2, x - 0.1, '%s' % y)\n",
    "plt.savefig('./report2.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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